
XFR baseline. We provide a baseline for XFR based on five algorithms for network attention evaluated on three publicly available convolutional networks trained for face recognition: LightCNN [1]}, VGGFace2 [2]} and ResNet-101. These baselines include two new algorithms for network attention called subtree EBP (Sec. REF ) and DISE (Sec. REF ).

Inpainting game protocol and dataset. We provide a standardized evaluation protocol (Sec. REF ) and dataset (Sec. REF ) for fine grained discriminative visualization of faces. This provides a quantitative metric for objectively comparing XFR systems.

XFR evaluation. We provide the first comprehensive evaluation of XFR using the baseline algorithms on the inpainting game protocol to provide a benchmark for future research (Sec. REF ). Furthermore, in the supplemental material, we show a qualitative evaluation on novel (non inpainted) images to draw conclusions about the utility of the methods for explanation on real images.

